April 2016
Intermediate to advanced
170 pages
3h 48m
English
Content preview from Distributed Computing with PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Chapter 3. Parallelism in Python
We mentioned threads, processes, and in general, parallel programming in the previous two chapters. We talked, at a very high level and very much in abstract terms, about how you can organize code so that some portions run in parallel, potentially on multiple CPUs or even multiple machines.
In this chapter, we will look at parallel programming in more detail and see which facilities Python offers us to make our code use more than one CPU or CPU core at the time (but always within the boundaries of a single machine). The main goal here will be speed for CPU-intensive problems, and responsiveness for I/O-intensive code.
The good news is that we can write parallel programs in Python using just modules in the standard ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.